1. Introduction
Summer precipitation in eastern China is primarily driven by the East Asian summer monsoon, which accounts for the largest proportion of the annual rainfall (Wu et al. 2003; Zhang 2015). As the most densely populated and important agricultural region in China, eastern China experiences profound impacts on its regional agricultural production and economic activities due to floods and droughts (Hu et al. 2020). Although many studies have investigated the possible mechanisms driving summer precipitation in eastern China, the majority have focused on seasonal mean precipitation for June–August (JJA). However, summer precipitation in eastern China exhibits distinct monthly variations as the rain belt advances from south to north (Chen et al. 2004), leading to considerable differences across different summer months (Xu et al. 2017; Zhang et al. 2021; Xue et al. 2022; Liu et al. 2023). Due to these variations, predicting precipitation anomalies separately for May–June and July–August can evidently improve the seasonal prediction skill (Wang et al. 2009). In particular, precipitation in July shows stronger interannual variability compared to August and usually leads to severe disasters in eastern China (Chen et al. 2022), suggesting a growing necessity for a better understanding of the potential mechanisms governing July precipitation.
Sea surface temperature (SST) has been recognized as an important forcing for summer precipitation in eastern China, and extensive studies have investigated the possible impacts of SST anomalies in the Atlantic, Pacific, and Indian Oceans (Feng et al. 2016; Ren et al. 2021; Wu 2017). Observational analyses and numerical experiments suggest that the North Atlantic SST anomalies can trigger an eastward Rossby wave train from the North Atlantic to the mid–high latitudes of Eurasia, affecting the climate in eastern China (Li et al. 2019). Simultaneously, warm tropical Atlantic SST anomalies usually produce anomalous upward motion over the tropical Atlantic, leading to anomalous downward motion in the tropical Pacific and a strengthened western Pacific subtropical high (Hong et al. 2014; Chang et al. 2016; Zhao et al. 2020; Zuo et al. 2019; Ren et al. 2021). Despite the dissipation or development of SST anomalies in the tropical Pacific in summer, they can still have important impacts on the precipitation in eastern China. For example, the El Niño events in their decaying phase usually benefit the maintenance of the rain belt, resulting in more precipitation in the Yangtze River basin (Jin and Tao 1999; Huang et al. 2004; Zeng et al. 2011). Developed El Niño conditions correspond to a diploe mode of summer precipitation anomalies in eastern China (Wu et al. 2003; Feng et al. 2016; Wen et al. 2019). Additionally, SST anomalies in the Indian Ocean have been proposed to act as a capacitor linking previous winter tropical Pacific SST signals with summer precipitation anomalies in eastern China (Yang et al. 2007; Xie et al. 2009; Wu 2017).
Factors influencing summer precipitation in eastern China also include the land surface thermal conditions (Wu et al. 2014; Zhu et al. 2015; Dong et al. 2022). Preceding land thermal conditions have an important influence on the variation of summer monsoon precipitation in eastern China (D. Liu et al. 2017). As a key variable characterizing the thermal state of the land surface, soil moisture plays a substantial role in influencing the land surface energy balance (Seneviratne et al. 2010; Talib et al. 2021). Many studies have investigated the effect of soil moisture on summer precipitation in China (Zhan and Lin 2011; Zhang and Zuo 2011; Meng et al. 2014; Zuo and Zhang 2016). For instance, Shi et al. (2021) found a close linkage between the interannual variability of East Asian monsoon precipitation and the accurate simulation of soil moisture. Liu et al. (2014) showed that the initial conditions of soil moisture could influence the occurrence of extreme precipitation events in East Asia due to the strong memory of the soil moisture. Zhou et al. (2018) demonstrated that the intensity of summer monsoon in the Tibetan Plateau is affected by the east–west dipole anomalies of soil moisture in late spring. L. Liu et al. (2017) found that higher spring soil moisture in North China reduces the land–sea thermal difference, weakening the intensity of the East Asian summer monsoon. One recent study suggests that the influence of soil moisture on summer precipitation in eastern China could surpass that of El Niño events in some cases (Zhou et al. 2020). It is also found that wetter spring soil moisture in the Indochina Peninsula is associated with less summer precipitation in the middle and lower reaches of the Yangtze River (Gao et al. 2020; Zhu et al. 2021).
Accurate prediction of July precipitation in eastern China is of great importance, yet it remains a big challenge due to the numerical model bias and limited understanding of the preceding factors driving precipitation anomalies (Yim et al. 2016; Liu et al. 2019). Previous studies mainly focus on the impacts of SST anomalies on July precipitation in eastern China (Li et al. 2023), while less attention has been paid to the effects of the land surface. This study aims to identify the leading modes of July precipitation in eastern China and investigate their associated preceding surface thermal anomalies of both ocean and land surface based on observational analysis and numerical experiments. This paper is organized as follows. Section 2 introduces data, model, and experimental design. Section 3 presents the leading modes of July precipitation anomalies in eastern China and their associated preceding underlying surface thermal signals. Section 4 shows the atmospheric circulation anomalies relevant to the surface thermal anomalies as well as their possible physical mechanisms. Numerical results are presented in section 5. Finally, section 6 provides a summary and discussion.
2. Data and methods
a. Data
The datasets used in this study are summarized in Table 1. Reanalysis data from the Climate Forecast System Reanalysis (CFSR; Saha et al. 2010) and Climate Forecast System, version 2 (CFSv2; Saha et al. 2014), were used. These data consist of various variables such as 0–10-cm soil moisture, surface pressure, air temperature, specific humidity, wind, and geopotential heights. The CFSR data cover the period from 1979 to 2010, while CFSv2 covers the period from 2011 to 2019. Both datasets have a horizontal resolution of 0.5° × 0.5° and contain 37 vertical layers. To minimize the uncertainty, three additional soil moisture datasets were utilized. The fifth generation of European Centre for Medium-Range Weather Forecasts (ECWMF) reanalysis data, known as ERA5 (Hersbach et al. 2023), cover the period from 1979 to 2019 with a spatial resolution of 0.25° × 0.25° and a surface soil thickness of 0–7 cm. The Global Land Data Assimilation System (GLDAS) data, produced by the National Aeronautics and Space Administration (NASA) (Rodell et al. 2004), covers the period from 1979 to 2019. The monthly 0–10-cm soil moisture data are generated by the Noah land surface model, with a horizontal resolution of 1.0° × 1.0°. The GLDASv2.0 from 1979 to 2014 and GLDASv2.1 from 2015 to 2019 are used. We calculated the difference in mean soil moisture for the period from 2000 to 2014 where GLDAS-2.0 and GLDAS-2.1 overlap, and adjusted the systematic differences to GLDAS-2.1 during 2015–19. The GLDASv2.0 from 1979 to 2014 and GLDASv2.1 from 2015 to 2019 were then merged to form a continuous long-term dataset, which covers the same period of other variables. The European Space Agency (ESA) in the Climate Change Initiative (CCI) project (ESA-CCI; Dorigo et al. 2017) provided the multisatellite surface soil moisture dataset, known as the ESA-CCI combined soil moisture version 06.1, which spans from 1979 to 2019 and has a spatial resolution of 0.25° × 0.25°.
Summary of datasets used in this study.
The SST data were obtained from the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST; Rayner et al. 2003), which covers the period from 1979 to 2019, with a spatial resolution of 1.0° × 1.0°. Monthly land precipitation data were provided by the Climatic Research Unit (CRU) gridded Time Series, version 4.04 (Harris et al. 2020), based on a collection of observed meteorological data from different regions of the world. These data span from 1979 to 2019 with a spatial resolution of 0.5° × 0.5°. The Global Precipitation Climatology Project (GPCP) dataset is produced by NASA Goddard Space Flight Center, covering the period from 1979 to 2019 with a spatial resolution of 2.5° × 2.5°.
b. Model and numerical experiments
The Community Earth System Model (CESM), version 2.2.0, was used to investigate the potential mechanism by which surface thermal factors affect July precipitation in eastern China via sensitivity experiments. The CESM2 model is a fully coupled global climate model that can simulate the past, present, and future climate of Earth (Danabasoglu et al. 2020). The model includes various modules including atmosphere, land surface, ocean, sea ice, land ice, ocean waves, and surface runoff. In our study, the Community Atmosphere Model, version 6.0 (CAM6; Craig et al. 2021), is coupled with the Community Land Model, version 5 (CLM5; Lawrence et al. 2019). Thus, only the atmosphere (CAM6) and land surface (CLM5) components were used in coupling. In the control (CTL) run, the model is forced with monthly climatological HadISST SST and sea ice data for the period of 1990–2019, and other external forcing data were also provided by the model developer. The simulations were performed at a horizontal resolution of 1.25° (longitude) × 0.9° (latitude) with 30 vertical layers described by the σ-p mixed vertical coordinates.
Six sensitivity experiments were conducted to explore the effects of SST anomalies in the tropical Pacific Ocean (SST_TPO run), the North Atlantic (SST_NAT run), and the Indian and tropical Pacific Oceans (SST_IND_TPO run) and the influence of Indochina Peninsula soil moisture (SM_INDO run), as well as the synergistic effects of SST and soil moisture anomalies (SM_SST run) on July precipitation in eastern China. The details of sensitivity experiments will be introduced in the associated section. Each experiment includes 30 ensemble members with slightly different initial conditions. Each member is integrated for 2 months from 1 June to 31 July.
3. Leading modes of precipitation anomalies and preceding surface thermal signals
Previous research has pointed out that El Niño–South Oscillation (ENSO) and the North Atlantic tripole-like SST anomalies can be used as potential predictors for July precipitation in eastern China (Chen et al. 2022; Chen and Li 2023). In this section, the antecedent signals of both SST and soil moisture for the dominant precipitation modes in eastern China during July are explored. Figure 1 illustrates the first two empirical orthogonal function (EOF) modes of July precipitation in eastern China (20°–40°N, 105°–125°E) together with their corresponding time series. All data were detrended and normalized before the EOF analysis. The first two modes are well distinguished from each other and from other modes according to North’s test (North et al. 1982). The first EOF mode (EOF1) displays a north–south dipole pattern in precipitation anomalies and explains 24.1% of the total variance. As shown in Fig. 1a, the precipitation exhibits positive anomalies in the northern part of 30°N in eastern China and negative anomalies in southern China to the south of 30°N. The second EOF mode (EOF2) explaining 17.8% of the total variance displays tripolar anomalies of precipitation in eastern China (Fig. 1b). It shows negative anomalies in the Yangtze River basin and positive anomalies in North China and the coastal areas of South China. In the following analysis, we mainly focus on the relevant surface thermal factor in June, since the land surface signal has a shorter persistence compared to the oceanic signal and is more likely to influence the climate of the following month.
The (a) first EOF mode and (b) second EOF mode of July precipitation in eastern China. Time series of (c) first EOF mode and (d) second EOF mode. The hatched areas are significant at the 10% level based on the regression coefficients between EOF time series and July precipitation.
Citation: Journal of Climate 37, 19; 10.1175/JCLI-D-23-0748.1
a. Tropical Pacific and North Atlantic SST signals related to the EOF1 mode
For the EOF1 mode, there are weak signals on the land surface during June, so we mainly focus on the signals over oceans. It is noted that regional SST anomalies may exhibit varying effects under different ENSO conditions, as demonstrated by Zhang et al. (2014) who found different SST effects during years with strong and weak tropical Pacific SST anomalies. Following Zhu et al. (2021), we utilized the Oceanic Niño Index (ONI) to distinguish years with strong and weak ENSO episodes in the tropical Pacific during summer. The ONI is the moving average of the SST anomalies in the Niño-3.4 region (5°N–5°S, 120°–170°W) for 3 consecutive months. Weak ENSO years were defined as those with ONI values between −0.5° and 0.5°C during both May–July and June–August (1979, 1980, 1981, 1984, 1986, 1989, 1990, 1993, 1994, 1995, 1996, 2001, 2003, 2005, 2006, 2012, 2013, 2014, 2016, 2017, and 2018), while strong ENSO years were defined as the remaining years (1982, 1983, 1985, 1987, 1988, 1991, 1992, 1997, 1998, 1999, 2000, 2002, 2004, 2007, 2008, 2009, 2010, 2011, 2015, and 2019). Previous researchers often use SST anomalies in the central and eastern Pacific during the previous winter to identify ENSO backgrounds (Dong 2016; Gao and Li 2023). This study is concerned with the impact of June SST anomalies on July precipitation in eastern China, and thus, SST anomalies in the central and eastern Pacific during this period are used to identify the ENSO background. To investigate the differences in SST anomalies under strong and weak ENSO signal years, we regressed June and July SST onto the EOF1 time index under different ENSO backgrounds. During strong ENSO signal years, significant SST anomalies dominate in the tropical Pacific from June to July (Figs. 2a,b). The significant SST anomalies in the Indian Ocean are weaker south of 30°S and gradually weaken from June to July. During the weak ENSO signal years, the ENSO signal in the tropical Pacific is absent from June to July, while the tripolar SST anomalies in the North Atlantic become stronger (Figs. 2c,d). It is suggested that the North Atlantic SST anomalies dominate July precipitation of EOF1 in eastern China during weak ENSO signal years, while the tropical Pacific SST anomalies play a major role during strong ENSO signal years. Based on the significant SST anomalies, the key areas of SST in June are selected and shown as the black rectangles in Figs. 2a and 2c.
Regressions of (a) June and (b) July SST (K) during strong ENSO signal years against EOF1 time index. (c),(d) As in (a) and (b), but for weak ENSO signal years. Regressions of July precipitation (mm day−1) against (e) the tropical Pacific SST index during June in the strong ENSO signal years and (f) the North Atlantic SST index during June in the weak ENSO signal years. All data were detrended, and the hatched areas are significant at the 10% level.
Citation: Journal of Climate 37, 19; 10.1175/JCLI-D-23-0748.1
Figures 2e and 2f show the spatial distribution of July precipitation anomalies regressed onto the tropical Pacific and North Atlantic SST indices in June. Notably, the tropical Pacific SST time series index was multiplied by −1 during the analysis to demonstrate the impact of negative SST anomalies. Results indicate a north–south dipole pattern of precipitation anomalies, consistent with the findings in Fig. 1a. Due to the differing climatic effects of SST anomalies in the tropical Pacific and North Atlantic, certain disparities exist between Figs. 2e and 2f. Specifically, the precipitation anomalies regressed against the tropical Pacific SST index indicate a generally more northward distribution, featuring north–south dipole anomalies divided by 32°N. Positive anomalies mainly appear in North China, whereas the large-value centers of negative anomalies are observed in the middle and lower reaches of the Yangtze River and the southern coastal areas of South China (Fig. 2e). Precipitation anomalies regressed against the North Atlantic SST index demonstrate closer similarity to the spatial distribution of the EOF1 mode, exhibiting significant negative (positive) anomalies in the northern area of 30°N. The positive anomaly centers are located in the east and west of northern China, while the large-value center of negative anomalies appears in the southwest of southern China (Fig. 2f).
b. Indian Ocean SST and Indochina Peninsula soil moisture signals related to the EOF2 mode
For the EOF2 mode, both preceding surface soil moisture and SST anomalies are considered since they both play important roles. Figures 3a–d depict the spatial distribution of the regression coefficients between EOF2 time series and June surface soil moisture from different datasets, with all data detrended before the analysis. The results show that the regression between the four soil moisture datasets and EOF2 is predominantly positive in large areas of the Indochina Peninsula, with CFSR/CFSv2 dataset exhibiting the strongest signal and GLDAS dataset showing the weakest signal. However, the results are quite divergent among these datasets in other regions. For example, the ESA-CCI dataset mainly shows positive anomalies in southern China with a few regions passing significance tests (Fig. 3a), whereas the other three datasets show negative anomalies in eastern China, with the areas of significant negative anomalies being the largest in the ERA5 dataset (Fig. 3c). Based on the consistent results from multiple soil moisture datasets, it can be concluded that the soil moisture in the Indochina Peninsula in June has significant positive anomalies with the EOF2 mode of precipitation in eastern China in July. Specifically, when the Indochina Peninsula soil moisture is higher in June, the precipitation in July will decrease in the middle and lower reaches of the Yangtze River and increase in North China and the coastal areas of southern China. To identify common and robust features in the results, the ensemble average of four datasets will be adopted in the subsequent analysis.
Spatial distribution regression coefficients between EOF2 time index and (a) ESA-CCI, (b) CFSR/CFSv2, (c) ERA5, and (d) GLDAS surface soil moisture, and (e) SST in June. All data were detrended before analysis. The hatched areas in (a)–(d) and dotted areas in (e) are significant at the 10% level.
Citation: Journal of Climate 37, 19; 10.1175/JCLI-D-23-0748.1
Figure 4 shows the spatial pattern of July precipitation anomalies obtained by multiple regression analysis using June soil moisture and SST factors. After removing the effects of SST, significant negative anomalies in the Yangtze River basin and positive anomalies in North China and southern China are obtained in July precipitation anomalies related to the Indochina Peninsula soil moisture in June (Fig. 4a), which is consistent with the findings of Fig. 1b. Moreover, significant positive anomalies in southern China and negative anomalies in the areas of northern China are related to the SST in the Indian Ocean (Fig. 4b). In contrast, the rainfall anomalies associated with the tropical Pacific SST display significant negative anomalies in the middle and lower reaches of the Yangtze River and significant positive anomalies in northern China (Fig. 4c). Consistent with previous analysis, the precipitation pattern related to the SST in the tropical Pacific in June shows dipole precipitation anomalies in the middle and lower reaches of the Yangtze River and northern China. In summary, the tripolar precipitation pattern is mainly associated with the Indian Ocean SST and Indochina Peninsula soil moisture anomalies in June.
Multiple linear regression of July precipitation (mm day−1) against (a) SMres, (b) T_INDres, and (c) TTPO in June. All data were detrended before analysis, and the hatched areas are significant at the 10% level.
Citation: Journal of Climate 37, 19; 10.1175/JCLI-D-23-0748.1
4. Observed pathways related to the influences of antecedent SST and soil moisture anomalies
a. Atmospheric circulation anomalies associated with two EOF modes
To reveal the linkages between EOF modes and the atmospheric circulation anomalies, Fig. 5 presents the spatial distribution of atmospheric circulation anomalies regressed onto the two EOF time series. In years with strong ENSO signal, high values of the EOF1 index correspond to significant and intensive easterly wind anomalies in the equatorial Pacific, along with significant and positive geopotential height anomalies in the northwestern Pacific at 850 hPa (Fig. 5b). In the mid- and high latitudes, a wave train pattern appears over the North Atlantic and Europe and is linked with the significant anomalies in the North Atlantic during July (Fig. 2d), which are inconsistent with the precursor signals discussed in this study. Meanwhile, the propagation of wave train shows a weak connection with anomalous atmospheric circulation in eastern China through analyzing wave activity flux (figure not shown). Therefore, the dipole precipitation anomalies in eastern China during strong ENSO signal years are mainly associated with the anomalous anticyclonic circulation, which is induced by the cold SST anomalies in the central and eastern Pacific. In years with weak ENSO signals, the anomalous atmospheric circulations related to the EOF1 index demonstrate a wave train from the North Atlantic to eastern China across the mid–high latitude of Eurasia at 200 hPa (Fig. 5c). Moreover, significant anticyclonic circulation anomalies appear in the northwestern Pacific, and significant cyclonic circulation anomalies are observed in the equatorial eastern Pacific at 850 hPa (Fig. 5d). Such atmospheric circulation anomalies may be related to the North Atlantic SST anomalies. Overall, the EOF1 mode is mainly associated with positive geopotential height anomalies in southern China, which corresponds to the northward displacement of the subtropical high. Then, more water vapor is transported to the north and the abnormal downward motion appears in southern China, leading to more precipitation in the north and less in the south over eastern China.
Regressions of (a) 200-hPa geopotential height (shaded; gpm) and (b) 850-hPa geopotential height (shaded; gpm) and wind field (vector; m s−1) in July against the EOF1 time series in the strong ENSO signal years. (c),(d) As in (a) and (b), but in the weak ENSO signal years. (e),(f) As in (a) and (b), but for EOF2 time series. The hatched areas and black vectors are significant at the 10% level.
Citation: Journal of Climate 37, 19; 10.1175/JCLI-D-23-0748.1
The EOF2 index corresponds to significant negative anomalies of geopotential height along with cyclonic circulation anomalies from southern China to the northern Indian Ocean (Fig. 5f). Meanwhile, there are significant positive anomalies of geopotential height over northeastern China. Unlike the EOF1 mode, the EOF2 mode is related to negative anomalies of geopotential height in southern China. Hence, the northeasterly wind anomalies over southern China weaken the monsoonal water vapor transport, resulting in a decrease in precipitation in the Yangtze River basin. The anomalous low pressure with abnormal upward motion in southern China and the anomalous water vapor transport to the north correspond to the increase in precipitation in the coastal area of southern China and northern China, respectively.
b. Role of SSTAs in tropical Pacific and North Atlantic in meridional dipole precipitation anomalies
SST anomalies play important roles in both large-scale atmospheric circulation and global climate. As noted in previous studies (Wang 2002; Wu et al. 2009; Zhu et al. 2014; Wen et al. 2020; Hu et al. 2021), the precipitation in eastern China can be influenced by central and eastern Pacific SST anomalies via the anomalous Walker and Hadley circulation. Yuan and Yang (2012) also pointed out that the anomalous Walker circulation associated with eastern Pacific El Niño would excite an anomalous local Hadley, which exhibits anomalous sinking motion over the equator and anomalous rising motion over eastern China in summer. Figure 6 shows July atmospheric circulation anomalies associated with the tropical Pacific SST anomalies in June. At 300 hPa (Fig. 6a), velocity potential shows significant positive anomalies in the central and eastern Pacific Ocean, while significant negative anomalies in the western Maritime Continent and Indian Ocean. On the other hand, the spatial distribution of velocity potential anomalies at 850 hPa shows a reversed pattern, displaying negative anomalies in the tropical central Pacific Ocean and positive anomalies in the Maritime Continent (Fig. 6b). Results indicate that cold SST in the tropical Pacific corresponds to an abnormal downward motion in the local region and an abnormal upward motion in the Maritime Continent, which contributes to a strengthened Walker circulation. Moreover, Fig. 6c presents the 850-hPa wind and streamfunction anomalies. The low-level wind field shows a Gill-type response pattern—the symmetrical anticyclonic anomalies on the north and south sides of the equator are triggered by cold SST anomalies over the central-eastern equatorial Pacific, accompanied by anomalous anticyclonic circulation and positive streamfunction anomalies in eastern China.
Regressions of (a) 300-hPa velocity potential (m2 s−1), (b) 850-hPa velocity potential (m2 s−1), (c) 850-hPa wind (vector; m s−1) and streamfunction (shaded; m2 s−1), and (d) meridionally averaged (5°S–5°N) and (e) zonally averaged (100°–120°E) vertical velocity (shaded; Pa s−1) and vertical circulation (vector) in July against the tropical Pacific SST index in the strong ENSO signal years. The vertical velocity is multiplied by −50, and the unit of horizontal wind is meters per second. All data were detrended before analysis, and the shaded areas are significant at the 10% level.
Citation: Journal of Climate 37, 19; 10.1175/JCLI-D-23-0748.1
Meanwhile, the enhanced Walker circulation further strengthens the Hadley circulation. To present a more detailed depiction of the abnormal vertical circulation, Fig. 6d shows the meridionally averaged (5°S–5°N) vertical velocity and circulation anomalies. Significant anomalous downward motion prevails in the eastern part of 160°E, extending from the surface to 200 hPa, while the westward region near 120°E exhibits significant anomalous upward motion. Combined with an anomalous westerly wind at the upper level and easterly wind at the lower level, it indicates an anomalous zonal overturning circulation across the equatorial Pacific. Moreover, the anomalous upward motion in the Maritime Continent reinforces the Hadley circulation, exerting an evident impact on the precipitation in eastern China. Figure 6e illustrates the zonally averaged (100°–120°E) vertical velocity and circulation anomalies. The region near 20°N is featured by significant positive anomalies of the vertical velocity, which corresponds to the anomalous downward motion in southern China. Consequently, the anomalous upward motion in the Maritime Continent enhances the Hadley circulation, inducing a stronger subtropical high and leading to suppressed precipitation in southern China. Moreover, the anomalous northward wind flow is conducive to more water vapor transport and precipitation in northern China.
As mentioned before, previous studies have disclosed the relationship between North Atlantic SST anomalies and precipitation in China through mid–high-latitude wave trains in Eurasia and zonal overturning circulation in the Pacific (Zuo et al. 2013; Chang et al. 2016). To examine the impacts of the North Atlantic SST anomalies on the atmosphere, Figs. 7a and 7b display regressions of July precipitation and 500-hPa vertical velocity in July against the North Atlantic SST index in the weak ENSO signal years. The results show significant positive anomalies of precipitation and negative anomalies of vertical velocity in the tropical Atlantic Ocean, indicating that increased SST causes significant increases in precipitation and anomalous upward motion. To further elucidate such a relationship, Fig. 7c displays the spatial distribution of regressed 200-hPa geopotential height and wave activity flux anomalies in July onto the North Atlantic SST index in June during weak ENSO signal years. Results indicate that there is a mid–high-latitude Eurasian Rossby wave train emanating from the North Atlantic and propagating downstream to eastern China. Geopotential height shows a significant negative anomaly over the North Atlantic Ocean and a significant positive anomaly to its northeastern side, followed by a significant negative anomaly over the Eastern European Plain and a significant positive anomaly over the eastern Ural Mountains. Eventually, there is a significant negative geopotential height anomaly in the south of Lake Baikal and a positive anomaly in eastern China, influencing the precipitation in July. The wave activity flux further confirms the propagation of the wave train, which originates from the North Atlantic, and bifurcates into two wave trains in Western Europe. Then, the wave trains combine together near the Mediterranean and continue to propagate eastward toward the south of Lake Baikal and eastern China.
Regressions of (a) precipitation (mm day−1), (b) 500-hPa vertical velocity (Pa s−1), (c) 200-hPa geopotential height (shaded; gpm) and wave activity flux (vector; m2 s−2), (d) zonal (shaded) and meridional (contour) wind flow (m s−1), and (e) 850-hPa wind (vector; m s−1) and streamfunction (shaded; m2 s−1) in July against the North Atlantic SST index in the weak ENSO signal years. All data were detrended before analysis. The dotted areas and black vectors in (c) are significant at the 10% level.
Citation: Journal of Climate 37, 19; 10.1175/JCLI-D-23-0748.1
Figure 7d shows the spatial distribution of the regression coefficients of 200-hPa zonal and meridional wind fields, with black lines indicating the meridional wind anomalies and the green solid line indicating the position of the 200-hPa jet stream. As illustrated in Fig. 7c, significant anomalies of the zonal wind are observed mainly in the east of Lake Balkhash and north of Lake Baikal, with significant negative anomalies around Lake Balkhash, significant positive anomalies in North China, and significant negative anomalies in South China. The positive zonal wind anomalies in North China reinforce the East Asian subtropical jet, which has a close association with the location of the summer rain belt in eastern China (Volonté et al. 2022). The enhanced East Asian subtropical jet and weakened East Asian polar front jet contribute to the increase in precipitation in northern China and the decrease in precipitation in southern China (Huang et al. 2014; Zhu et al. 2016). Consistent with the findings of He et al. (2017), the dipolar anomalies of summer precipitation in eastern China are mainly related to the strengthening and weakening of the East Asian subtropical jet. In addition, the meridional wind anomalies also display a west-to-east propagation characteristic, with positive anomalies in the North Atlantic and the west of the Ural Mountains and negative anomalies over the Eastern European Plain and Lake Balkhash. Finally, dipolar anomalies appear in China.
Figure 7e shows the spatial distribution of regressed 850-hPa wind and streamfunction in July. There is anomalous upward motion in the tropical Atlantic in southern North America and cyclonic anomalies in the lower atmosphere. Conversely, anticyclonic anomalies appear in the central and west Pacific, which significantly decrease the precipitation in southern China. These results are similar to the anomalies in Fig. 5d. Overall, the North Atlantic SST anomalies can also influence precipitation in eastern China through westward atmospheric teleconnections. Additionally, positive SST anomalies in the tropical Atlantic favor convective activity, inducing an abnormal convergence in the lower troposphere and an abnormal divergence in the upper troposphere. The anomalous cyclonic circulation in the lower atmosphere in the tropical North Atlantic and eastern Pacific resembles the Gill-type Rossby wave response. Meanwhile, abnormal convergence in the upper troposphere and abnormal divergence in the lower troposphere occur in the central Pacific, corresponding to abnormal downward motion and suppressed convective activities in the central Pacific. Finally, an abnormal anticyclone is formed in the lower atmosphere of the western Pacific, leading to a strengthened western Pacific subtropical high. Feng and Chen (2022) referred to this atmospheric response as the Rossby wave–induced chain response of convergence and divergence caused by the tropical Atlantic SST anomalies.
c. Role of Indian Ocean SST and Indochina Peninsula soil moisture anomalies in meridional tripolar precipitation anomalies
Figure 8 displays the velocity potential at 300 and 850 hPa, and low-level atmospheric circulation regressed against T_INDres. The regressed 300-hPa velocity potential anomalies show significant positive anomalies over the Indian Ocean and significant negative anomalies over the western Pacific (Fig. 8a). A roughly opposite anomalous pattern is found at 850 hPa (Fig. 8b). The cold SST anomalies in the Indian Ocean correspond to an abnormal downward motion in the local region and an abnormal upward motion in the western Pacific. Accordingly, the cold Indian Ocean SST tends to strengthen the Walker circulation and influence the atmospheric circulations in eastern China. There are significant cyclonic circulation anomalies and negative streamfunction over the northwestern Pacific, which will increase the precipitation in the coastal area of southern China. The northeasterly wind anomalies in the north flank of the cyclonic circulation anomalies reduce the water vapor transport and precipitation in the Yangtze River basin. Previous studies have also demonstrated that the persistent SST anomalies over the tropical north Indian Ocean induce the anomalous western North Pacific anticyclone (Wu et al. 2010; Xie et al. 2016; Li et al. 2017; Feng and Chen 2021). Yu and Sun (2024) also showed that the cold SST anomalies in the tropical Indian Ocean depress the local convective activity and strengthen upward motion in the western tropical Pacific, which changes the Walker circulation and favors the low-level anomalous cyclone over the western tropical Pacific, which is similar to the results in this study.
Regressions of (a) 300-hPa velocity potential (m2 s−1), (b) 850-hPa velocity potential (m2 s−1), and (c) 850-hPa wind (vector; m s−1) and streamfunction (shaded; m2 s−1) in July against the T_INDres index. All data were detrended before analysis. The dotted areas and black vectors are significant at the 10% level.
Citation: Journal of Climate 37, 19; 10.1175/JCLI-D-23-0748.1
For the impacts of June soil moisture in the Indochina Peninsula, when the Indochina Peninsula soil moisture is wetter, the 700-hPa geopotential height shows negative anomalies along with cyclonic circulation anomalies around the Indochina Peninsula and an anomalous anticyclone in eastern China (Fig. 9a). The associated northeasterly wind anomalies in southern China reduce the water vapor transport toward the Yangtze River basin. Meanwhile, the significant water vapor divergence is found in the middle and lower reaches of the Yangtze River, corresponding to a significant decrease in precipitation, while the significant water vapor flux convergence in southwestern China and the Indochina Peninsula is related to an increase in precipitation (Fig. 9b). It is noted that the atmospheric anomalies are generally located in the east of the Indochina Peninsula. Previous studies also suggested that the land surface anomalies in the Indochina Peninsula can lead to anomalous cyclonic or anticyclonic circulation and thus influence subtropical high in eastern China (Kanae et al. 2001; Shi et al. 2008; Liu et al. 2010; Wu et al. 2014; Gao et al. 2019; Dong et al. 2024). In fact, the intensification or weakening of the western Pacific subtropical high can cause widespread atmospheric circulation anomalies, with a large portion located over the South China Sea and the northwest Pacific. However, atmospheric anomalies in reanalysis data can be disturbed by many other climatic factors. The East Asian summer monsoon is a complicated system, and we want to prove that soil moisture in the Indochina Peninsula will contribute to the precipitation anomalies in eastern China in this study. Therefore, in section 5, we conducted numerical experiments to help us distinguish the influence of soil moisture. Compared to the influence of Indochina Peninsula soil moisture anomalies in May (Dong et al. 2022), the soil moisture in June also exerts an impact on the precipitation in eastern China during July by inducing locally cyclonic anomalies. Notably, the main difference is the position of the anomalous anticyclonic circulation on the north side of the cyclonic circulation anomalies. In July, the anomalous anticyclone shifts west-northward, consequently causing the easterly wind anomalies to shift northward and affect the precipitation in the Yangtze River basin. The main reason for this alteration may be attributed to the difference in atmospheric general circulation in June and July. The subtropical high in July lies further north than that in June, leading to westerly wind in June and southwesterly wind in July at midtroposphere over southern China. The strong southerly wind may promote the northward movement of the anomalous anticyclonic circulation.
Spatial distribution of multiple linear regression of (a) 700-hPa geopotential height (shaded; gpm) and wind field (vector; m s−1), and (b) vertically integrated water vapor flux (vector; kg m−1 s−1) and water vapor convergence (shaded; kg s−1) from 1000 to 300 hPa in July against SMres in June. (c) Climatology of meridional-mean (10°–22°N) vertical circulation (vector), and vertical transport of water vapor (shaded; kg kg−1 s−1) on a longitude–pressure cross section during July. (d) Regression of meridional-mean (10°–22°N) vertical transport of water vapor (shaded; kg kg−1 s−1) and Q2 (contour; K day−1), and (e) dynamic and (f) thermodynamic terms of vertical water vapor transport (kg kg−1 s−1) in July against SMres in June on a longitude–pressure cross section. All the data are linearly detrended. Vertical velocity (Pa s−1) is multiplied by −50, and the unit of horizontal wind is meters per second. All the data are linearly detrended and standardized before regression. The shaded areas in (a), the black vectors in (b), and the hatched areas in (d)–(f) are significant at the 10% level.
Citation: Journal of Climate 37, 19; 10.1175/JCLI-D-23-0748.1
In addition, the formation of the anomalous cyclonic circulation in the Indochina Peninsula can be related to the local upward water vapor transport and condensational heating. Figure 9c presents the meridionally averaged (10°–22°N) climatological vertical circulation and water vapor vertical transport [
Figures 9e and 9f show the anomalies of dynamic and thermodynamic terms of vertical water vapor transport. It reveals that the significant increase in upward water vapor transport caused by abnormal ascent mainly appears in the higher troposphere, while the significant increase in upward water vapor transport caused by water vapor occurs in the lower troposphere. It is indicated that the upward water vapor transport in the lower atmosphere of the Indochina Peninsula is mainly caused by the increase in water vapor that is related to wetter soil in the Indochina Peninsula. Therefore, the increase in the Indochina Peninsula soil moisture can lead to enhanced evapotranspiration, resulting in moisture increase in the lower troposphere. In conjunction with the upward motion and water vapor transport in the Indochina Peninsula, the release of latent heat contributes to the formation of anomalous cyclone around the local area.
5. Verifications of SST and soil moisture effects based on sensitivity experiments with CAM6
a. Simulated atmospheric responses to tropical Pacific and North Atlantic SSTAs
The SST_TPO and SST_NAT experiments were conducted to investigate the impacts of SST anomalies on precipitation. The SST anomalies were added to the corresponding months in the tropical Pacific Ocean (30°S–40°N, 120°E–60°W) and North Atlantic (10°S–55°N, 80°W–0°) regions based on the regression results in Fig. 2, respectively. Figure 10 provides the differences in July precipitation between sensitivity experiments and CTL, showing dipole patterns of precipitation anomalies in eastern China, which are similar to the results in Figs. 2e and 2f. Specifically, the SST_TPO experiment also shows significant positive precipitation anomalies on the north side of 30°N and significant negative precipitation anomalies in southern China (Fig. 10a). The SST_NAT experiment exhibits significant negative precipitation anomalies in southern China and significant positive precipitation anomalies in northern China with a boundary at 30°N (Fig. 10b). However, the simulation results do not accurately capture the center of negative anomalies observed in the southern coastal areas of southwestern China due to possible bias of the model.
Spatial distribution of the difference in precipitation (mm day−1) during July between (a) SST_TPO and CTL, and (b) SST_NAT and CTL. The hatched areas are significant at the 10% level.
Citation: Journal of Climate 37, 19; 10.1175/JCLI-D-23-0748.1
Regarding the atmospheric responses for the central Pacific SST anomalies in the model, the differences in the July velocity potential at 300 and 850 hPa between SST_TPO and CTL are provided in Figs. 11a and 11b, respectively. The 300-hPa velocity potential shows significant positive anomalies in the tropical Pacific and significant negative anomalies in the Maritime Continent and Indian Ocean. Conversely, at 850 hPa, negative anomalies of velocity potential appear in the tropical central Pacific and positive anomalies of velocity potential occur in the Maritime Continent. It suggests that the local lower atmosphere divergences and the upper atmosphere convergences are accompanied with anomalous downward motion when the tropical Pacific SST decreases. In contrast, the atmosphere convergences in the lower troposphere and divergences in the upper troposphere correspond to anomalous upward motion in the Maritime Continent. Such anomalous vertical circulation could enhance the Walker circulation, which is generally consistent with the observed results. For the differences in low-level atmospheric circulation, there are anomalous anticyclonic circulation and significant positive anomalies of streamfunction on the north side of the equator (Fig. 11c). Notably, the anticyclonic circulation anomalies are stronger and more northwestward in the western Pacific than those in observations, leading to the northward movement of anomalous cyclonic circulation around Japan. Nevertheless, the anomalous anticyclonic circulation may strengthen the subtropical high in the western Pacific, resulting in decreased precipitation in southern China and increased precipitation in northern China.
Differences (SST_TPO − CTL) of (a) 300-hPa velocity potential (m2 s−1), (b) 850-hPa velocity potential (m2 s−1), (c) 850-hPa wind (vector; m s−1) and streamfunction (shaded; m2 s−1), and (d) meridionally averaged (5°S–5°N) and (e) zonally averaged (100°–120°E) vertical velocity (shaded; Pa s−1) and vertical circulation (vector) in July. The vertical velocity is multiplied by −50, and the unit of horizontal wind is meters per second. The dotted areas are significant at the 10% level.
Citation: Journal of Climate 37, 19; 10.1175/JCLI-D-23-0748.1
Figure 11d further provides a more detailed illustration of the anomalous vertical circulation that strengthens the Walker circulation. The significant abnormal downward motion mainly occurs from the surface to 150 hPa in the east of 180°. On the western side, there is a significant abnormal upward motion at 120°E–180°. Coordinated with the abnormal westerly wind of 300–150 hPa and the abnormal easterly wind of 850–500 hPa, the Walker circulation is strengthened. Generally, the simulated results are similar to the observations, but the stimulated anomalies of vertical circulation exhibit an eastward displacement at low level and westward at high level relative to the observation (Fig. 11d). It may be due to the biases of zonal wind in the model, which leads to the climatological Walker circulation more easterly at lower troposphere and more westerly at higher troposphere relative to the observation. In addition, the anomalous upward motion in the Maritime Continent induces an anomalous downward motion in southern China (Fig. 11e). These findings suggest that the decreased tropical Pacific SST can lead to the formation of an abnormal vertical circulation that rises in the Maritime Continent and sinks in southern China, leading to a stronger Western Pacific subtropical high.
Regarding atmospheric circulation anomalies induced by the North Atlantic SST anomalies, Fig. 12a presents the differences in July precipitation and 500-hPa vertical velocity. There are significant positive (negative) anomalies of precipitation (vertical velocity) in the northwestern and tropical North Atlantic Ocean, corresponding to the increased precipitation (upward motion). Thus, both the observed and numerical results suggest the impacts of the North Atlantic SST anomalies on atmosphere, which trigger the mid–high-latitude Eurasian Rossby wave train. Figure 12c presents the difference in July geopotential height and wave activity flux at 200 hPa between the SST_NAT and CTL. It shows a negative anomaly over the North Atlantic and a positive anomaly to its northeast. This is followed by a negative anomaly over the Eastern European Plain, a positive anomaly in the eastern Ural Mountains, a negative geopotential height anomaly around Lake Baikal, and a positive geopotential height anomaly in eastern China. It is noted that the geopotential height anomalies at 200 hPa show some differences especially in the North Atlantic between simulated and observed results. By comparing the precipitation and vertical velocity anomalies over the North Atlantic in Figs. 7 and 12, it can be seen that although their distributions are similar, the model shows stronger positive precipitation anomalies and upward motion in the western North Atlantic compared to the observations. Hence, the atmospheric responses in the model are more westward in the North Atlantic and Europe. However, the distribution of the wave train and potential height anomalies is generally similar to the observations, featured by an eastward propagation of the Eurasian mid–high-latitude wave train originated from the North Atlantic, which is also reflected in wave activity flux anomalies.
Differences (SST_NAT − CTL) in (a) precipitation (mm day−1), (b) 500-hPa vertical velocity (Pa s−1), (c) 200-hPa geopotential height (shaded; gpm) and wave activity flux (vector; m2 s−2), (d) zonal (shaded) and meridional wind flow (vector; m s−1), and (e) 850-hPa wind (vector; m s−1) and streamfunction (shaded; m2 s−1) in July. The dotted areas are significant at the 10% level.
Citation: Journal of Climate 37, 19; 10.1175/JCLI-D-23-0748.1
Figure 12d shows differences in zonal and meridional wind fields at 200 hPa. The anomalous meridional wind exhibits a basic characteristic of propagation from west to east. The zonal wind is featured by negative anomalies around Lake Baikal and southern China and positive anomalies in northern China, which are basically consistent with the distribution in Fig. 7d. Therefore, both observed and numerical results suggest that the positive zonal wind anomalies in northern China strengthen the East Asian subtropical jet, favoring dipole precipitation anomalies in eastern China. Additionally, the wind field and streamfunction at 850 hPa displays an atmospheric response of Gill-type Rossby waves, which shows significant cyclonic circulation anomalies in the eastern tropical Pacific (Fig. 12e). There is a significant anticyclonic anomaly in the northwestern Pacific, causing a strengthened western Pacific subtropical high in eastern China. These results are generally consistent with the observational data, indicating the potential impact of North Atlantic SST anomalies on the atmospheric circulation pattern.
b. Simulated atmospheric responses to anomalous Indian Ocean SST and Indochina Peninsula soil moisture
To investigate the effects of Indian Ocean SST, Indochina Peninsula soil moisture, and their synergistic influence, we conducted three sensitivity experiments. In the SST_IND_TPO experiment, SST anomalies are added in the Indian Ocean (20°S–25°N, 40°–100°E) and tropical Pacific (10°S–10°N, 80°W–180°) based on results in Fig. 3e, to confirm the role of EOF2-related SST anomalies in Indian and Pacific Oceans. In the SM_INDO experiment, the soil moisture anomalies in the Indochina Peninsula region (10°–22°N, 90°–110°E), which are based on the averaged results in Figs. 3a–d, are added in the first three soil layers (0–12 cm) of the CTL run to create gridded data used in sensitivity experiment. Then, the surface soil moisture in the land cell of the Indochina Peninsula is prescribed during the whole of June in SM_INDO. For regions outside the Indochina Peninsula, the surface soil moisture is substituted with the average June soil moisture from the CTL run, aiming to mitigate the atmospheric responses triggered by soil moisture anomalies in these other areas. Finally, the SST_SM experiment adds the soil moisture and SST anomalies simultaneously to explore their potential synergistic effects.
Figure 13 shows the differences in precipitation in eastern China during July for SST_IND_TPO, SM_INDO, and SST_SM experiments. The precipitation anomalies generally exhibit a tripolar distribution in eastern China in three sensitivity experiments. Specifically, when imposing negative SST anomalies in the Indian Ocean and tropical Pacific Ocean (Fig. 13a), simulated precipitation is mainly reduced between 30° and 35°N and increased in the north of 35°N and south of 30°N in eastern China. The model simulation can largely reproduce the observed precipitation anomalies with a slightly northward shift in eastern China When wetter soil moisture is imposed in the Indochina Peninsula (Fig. 13b), the Yangtze River basin experiences a significant decrease in precipitation and northern China and the coast of South China receive more precipitation. These results are basically consistent with the observed results in Fig. 4b. When both soil moisture and SST anomalies are added in the model (Fig. 13c), a significant decrease of the simulated precipitation is found between 25° and 33°N in eastern China and an increase in precipitation occurs in northern China and southwestern China. It is noteworthy that the synergistic effects of soil moisture and SST on precipitation in eastern China are stronger than their individual effects. For the precipitation anomalies in the Yangtze River basin (28°–34°N, 108°–122°E), the synergistic effect is 14% stronger than the effect of SST alone and 42% stronger than the effect of soil moisture alone.
Differences in precipitation (mm day−1) in July. Subtraction of the CTL from (a) SST_IND_TPO, (b) SM_INDO, and (c) SST_SM experiment. The hatched areas are significant at the 10% level.
Citation: Journal of Climate 37, 19; 10.1175/JCLI-D-23-0748.1
In the SST_IND_TPO experiment, SST anomalies in the Indian and tropical Pacific Oceans related to EOF2 have been included. The results display significant positive anomalies of velocity potential over the Indian Ocean, significant negative anomalies over the western Pacific at 300 hPa (Fig. 14a), and an opposite distribution of anomalies at 850 hPa (Fig. 14b). Moreover, the streamfunction and wind anomalies at 850 hPa also show similar negative anomalies of geopotential height and cyclonic circulation anomalies located in northwestern Pacific (Fig. 14c). The anomalous northeasterly wind in southern China inhibits water vapor transport to the Yangtze River basin, resulting in decreased precipitation in that area. These findings are similar to the observed anomalies linked with cold Indian Ocean SST, although the velocity potential anomalies over the western Pacific are more westerly than those in observation. We have also conducted experiments with only the Indian Ocean SST anomalies, which show similar atmospheric circulation anomalies and further prove the predominant influence of Indian Ocean SST anomalies in the model. Moreover, the cold SST anomalies in the eastern equatorial Pacific also have some impacts on the atmosphere in eastern China. Through meridionally averaged (0°–20°N) vertical circulation anomalies (figure not shown), the anomalous downward motion induced by cold SST anomalies in the eastern equatorial Pacific shows a weak contribution to the formation of anomalous cyclonic circulation and upward motion in eastern China. Overall, sensitivity experiment suggests the impacts of Indian Ocean SST anomalies on the atmosphere, which are generally consistent with the observed results.
Differences (SST_IND_TPO − CTL) of (a) 300-hPa velocity potential (m2 s−1), (b) 850-hPa velocity potential (m2 s−1), and (c) 850-hPa wind (vector; m s−1) and streamfunction (shaded; m2 s−1) in July. The dotted areas and black vectors are significant at the 10% level.
Citation: Journal of Climate 37, 19; 10.1175/JCLI-D-23-0748.1
Figure 15 shows the atmospheric changes caused by June soil moisture anomalies. The shaded regions denote significant anomalies in geopotential height, and the black vectors show significant anomalies in the wind field. When the Indochina Peninsula soil moisture increases, significant negative anomalies of geopotential height accompanied with cyclonic circulation anomalies are found extending from the Indochina Peninsula to the South China Sea at 700 hPa (Fig. 15a). In the northern flank of the cyclonic circulation, the easterly and northeasterly wind anomalies in southern China will weaken the transport of water vapor to the Yangtze River basin. Figure 15b shows the differences in water vapor flux and convergence. The southwestward wind anomalies in southern China reduce the water vapor transport to the Yangtze River basin, and the divergence of water vapor flux in the Yangtze River basin results in a significant decrease in precipitation. The convergence of water vapor flux in northern China and the coastal area of southern China favors more precipitation in these areas. However, there are still some differences between the results in reanalysis data and sensitivity experiments. For instance, the location of the 700-hPa cyclonic anomalies and precipitation anomalies in model results is further northeast than that in reanalysis data. In the CTL run, the climatological western Pacific subtropical high is similarly located further north and east compared to observations during July (figure not shown), which may influence the simulated anomalies. Moreover, the stronger low-level southwesterly in the model over the Indochina Peninsula may also result in a northeastward shift of these atmospheric circulation anomalies. In fact, the model cannot fully reproduce the conditions in observation and the biases in the numerical experiments may induce the discrepancies. In studies by Zhu et al. (2021) and Tang et al. (2023), they also found that the atmospheric anomalies in the CAM model and observations show some differences due to the model biases.
Differences in (a) 700-hPa geopotential height (shaded; gpm) and wind field (vector; m s−1), and (b) water vapor flux (vector; kg m−1 s−1) and water vapor convergence (shaded; kg s−1) from 1000 to 300 hPa in July between SM_INDO and CTL. (c) Climatology of meridional-mean (10°–22°N) vertical circulation (vector), and vertical transport of water vapor (shaded; kg kg−1 s−1) on a longitude–pressure cross section during July in the CTL run. (d) Differences in meridional-mean (10°–22°N) vertical transport of water vapor (shaded; kg kg−1 s−1) and Q2 (contour; K day−1), and (e) dynamic and (f) thermodynamic terms of vertical water vapor transport in July on a longitude–pressure cross section between SM_INDO and CTL. Vertical velocity (Pa s−1) is multiplied by −50, and the unit of horizontal wind is meters per second. The shaded areas in (a), hatched areas in (d), and dotted areas and black vectors in (b) are significant at the 10% level.
Citation: Journal of Climate 37, 19; 10.1175/JCLI-D-23-0748.1
Furthermore, the simulated vertical distribution of the circulation and the vertical transport of water vapor are similar to the observed results, showing upward motion with upward water vapor transport over the Indochina Peninsula (Fig. 15c), although their magnitudes in CTL are slightly underestimated. In addition, the differences in vertical water vapor transport and condensational heat also show significant positive anomalies from 850 to 300 hPa near 102°E (Fig. 15d), and similar to the observations, the upward water vapor transport in lower troposphere is mainly contributed by the variation of water vapor (Fig. 15f). Hence, it is concluded that the increased soil moisture in the Indochina Peninsula can enhance evapotranspiration and air moisture in the lower atmosphere. With the updraft above the Indochina Peninsula, the increased water vapor is transported upwardly to the higher atmospheric levels and releases condensational heat, which is conducive to the formation of an anomalous cyclonic circulation and increased precipitation in the Indochina Peninsula.
6. Summary and conclusions
Based on observational analysis and numerical simulation, this study investigated the potential oceanic and land surface drivers of July rainfall in eastern China by examining two dominant modes of precipitation anomalies.
The first EOF mode of July precipitation in eastern China reveals a north–south dipole pattern, which is mainly affected by the antecedent SST anomalies. In years with a strong tropical Pacific ENSO signal, the dipolar precipitation pattern is mainly related to SST anomalies in the tropical central and eastern Pacific during June. In years with a weak tropical Pacific ENSO signal, tripole-like SST anomalies in the North Atlantic during June are the main driver. The second mode of July precipitation in eastern China displays a tripolar distribution that is significantly linked with the SST in the Indian Ocean and soil moisture in the Indochina Peninsula during June. As SST and soil moisture anomalies persist from June to July, they influence July precipitation in eastern China by altering the atmospheric circulation. Both observed analysis and numerical experiments further reveal the relevant physical mechanisms. Results suggest that the tropical Pacific SST anomalies could influence the precipitation in eastern China through changes in the Walker and Hadley circulation. Specifically, cold SST anomalies in the tropical central and eastern Pacific can induce anomalous downward motion in the local region and anomalous upward motion in the Maritime Continent, strengthening the Walker circulation. Meanwhile, the anomalous updraft over the Maritime Continent enhances the Hadley circulation, resulting in a downdraft and weakened convective activity in southern China. Moreover, the North Atlantic SST anomalies could trigger the Eurasian mid–high-latitude wave train, which induces an anomalous anticyclonic circulation in eastern China. Meanwhile, the warm SST anomalies in the tropical Atlantic enhance local convective activities, and then, abnormal downward motion and anticyclonic circulation appear in the central Pacific Ocean through westward overturning circulation. These mechanisms are similar to the results demonstrated by Chen et al. (2022) and Zuo et al. (2019). Therefore, both tropical Pacific and North Atlantic SST anomalies can induce a strengthened western Pacific subtropical high in eastern China, which reduces the precipitation in southern China and increases the precipitation in northern China.
The second EOF mode of July precipitation is featured by meridional tripolar precipitation anomalies, which are mainly associated with both Indian Ocean SST and Indochina Peninsula soil moisture anomalies. Cold Indian Ocean SST strengthens the Walker circulation between the Indian Ocean and the northwestern Pacific, leading to more intense ascending motion over the northwestern Pacific. Consequently, an anomalous cyclonic circulation appears in the tropical western Pacific Ocean and decreases water vapor transport to the Yangtze River basin. These mechanisms are similar to the results found by Yu and Sun (2024). Similar cyclonic circulation anomalies can be triggered by wetter soil in the Indochina Peninsula, which can transport water vapor upwardly with the climatological updraft and then release condensational heat over the Indochina Peninsula. As a result, the water vapor transport toward the Yangtze River basin is weakened by the northeasterly wind anomalies over southern China, which decreases the precipitation in the Yangtze River basin. The anomalous northward water vapor transport and cyclonic circulation anomalies increase the precipitation in northern China and coastal areas of southern China, respectively. Numerical models further demonstrate that the combined effects of soil moisture and SST have a stronger impact on anomalous atmospheric circulation and precipitation than individual factors, intensifying northeasterly wind anomalies and decreasing precipitation in the Yangtze River basin.
This study employs observational data and numerical experiments to investigate the potential factors driving precipitation in eastern China during July. The schematic diagram is provided in Fig. 16. However, there are some limitations in this study. The CAM6 model cannot fully simulate the observed atmospheric circulation and precipitation characteristics. Hence, the precipitation simulated in the control experiment in eastern China is slightly shifted, which causes biases in the atmosphere and precipitation anomalies in the sensitivity experiment. Additionally, given the complex nature of the interaction process among various climatic factors affecting summer precipitation in eastern China (Wang et al. 2012; Gao et al. 2020; Liang et al. 2021), further research studies are needed to explore the physical mechanisms and contributions of SST and soil moisture and to understand how soil moisture interacts with SST to influence precipitation under the background of the East Asian monsoon.
Schematic diagram of the impacts of SST and soil moisture on the (a) EOF1 and (b) EOF2 modes of July precipitation over eastern China. The letters A and C indicate the centers of the anomalous anticyclone and cyclone circulation, respectively.
Citation: Journal of Climate 37, 19; 10.1175/JCLI-D-23-0748.1
Acknowledgments.
This study is supported by the National Key Research and Development Program of China (2022YFF0801603), the National Natural Science Foundation of China (Grant 42088101), the National Key Research and Development Program of China (2023YFC3206001), and the Major Science and Technology Program of the Ministry of Water Resources of China (SKS-2022034).
Data availability statement.
The CRU T.s.4.04 dataset is provided by the Climatic Research Unit (University of East Anglia) and Met Office, from the website at https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.04. The HadISST dataset was downloaded from the U.K. Met Office at https://www.metoffice.gov.uk. The CFSv2 reanalysis data are from https://rda.ucar.edu/datasets/ds094.2. ESA-CCI data are from https://climate.esa.int/en/projects/soil-moisture/data. The ERA5 monthly soil moisture data can be downloaded from https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels-monthly-means?tab=overview. GLDAS data are from https://disc.gsfc.nasa.gov/datasets/GLDAS_NOAH025_M_2.0/summary?keywords=GLDAS and https://disc.gsfc.nasa.gov/datasets/GLDAS_NOAH025_M_2.1/summary?keywords=GLDAS. The source code of CESM2 can be downloaded from the website at https://www.cesm.ucar.edu/models/cesm2.
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